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Last year, Concentric Sky announced the release of a tool it calls BadgeRank, which uses an algorithm to rank the relative value of badges using a number of criteria such as endorsements and outcomes (Harman, 2018)(PRWeb, 2018). BadgeRank is intended to be a search engine for open badges, but I think its potential uses don’t end there.

In this blog, I have advocated for a learner-centric approach to badge stacking that I call upside-down stacking. The idea is this: a learner adds a competency statement or goal to a badge pathway and then earns badges to support the claim of the competency statement or show progress toward the stated goal. It’s a flexible approach that enables learners to design their own pathways and earn as many badges and endorsements as they want to support their claims. The more badges and endorsements learners acquire, the more compelling their claims begin to look. To learn more about upside-down badge stacking you can read the blog post or view the presentation slides.

For this post, the important point about upside-down stacking is that it creates clusters of badges around competency statements, but does so in a way that is still compatible with tiered pathway designs.

BadgeRank’s algorithm, in order to actually rank badges, must use criteria to assign a weight to each badge. In a cluster of badges, the weights of the individual badges could be summed in order to determine a weight for the badge cluster. Because each badge cluster is associated with a competency statement, you get a weight associated with that competency statement.

It would take some thinking and refinement, but it’s conceivable that eventually we might be able to use the weight of a badge cluster to at least partly infer the likelihood of a learner’s claim to be developing or already possess a given competency.

The idea of an algorithm generating something like a competency score for a badge earner both excites and frightens me. I welcome your comments.